Vulnerability-Lookup

PYSEC-2026-2302

Vulnerability from pysec - Published: 2026-06-11 10:16 - Updated: 2026-07-13 05:52

Details

vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the VideoMediaIO.load_base64() method. When processing video/jpeg data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.

Severity

7.5 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Impacted products

Name	purl
vllm	pkg:pypi/vllm

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "ecosystem_specific": {},
      "package": {
        "ecosystem": "PyPI",
        "name": "vllm",
        "purl": "pkg:pypi/vllm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.8.0"
            },
            {
              "fixed": "0.19.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.10.0",
        "0.10.1",
        "0.10.1.1",
        "0.10.2",
        "0.11.0",
        "0.11.1",
        "0.11.2",
        "0.12.0",
        "0.13.0",
        "0.14.0",
        "0.14.1",
        "0.15.0",
        "0.15.1",
        "0.16.0",
        "0.17.0",
        "0.17.1",
        "0.18.0",
        "0.18.1",
        "0.8.0",
        "0.8.1",
        "0.8.2",
        "0.8.3",
        "0.8.4",
        "0.8.5",
        "0.8.5.post1",
        "0.9.0",
        "0.9.0.1",
        "0.9.1",
        "0.9.2"
      ]
    }
  ],
  "aliases": [
    "CVE-2026-5497",
    "GHSA-wcwg-c5fc-9vrc"
  ],
  "details": "vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.",
  "id": "PYSEC-2026-2302",
  "modified": "2026-07-13T05:52:25.563903Z",
  "published": "2026-06-11T10:16:21.903Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://access.redhat.com/security/cve/CVE-2026-5497"
    },
    {
      "type": "WEB",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-5497.json"
    },
    {
      "type": "REPORT",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2487813"
    },
    {
      "type": "FIX",
      "url": "https://github.com/vllm-project/vllm/commit/58ee61422169ce17e08248f8efa1e9df434fe395"
    },
    {
      "type": "EVIDENCE",
      "url": "https://huntr.com/bounties/7bd92629-b396-4449-8f88-6c0092530eb4"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-wcwg-c5fc-9vrc"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

CVE-2026-5497 (GCVE-0-2026-5497)

Vulnerability from cvelistv5 – Published: 2026-06-11 08:31 – Updated: 2026-07-22 12:08

Title

Unbounded Frame Count in video/jpeg Base64 Data URL Processing Leads to OOM DoS in vllm-project/vllm

Summary

vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.

Severity

7.5 (High)


                        
                          CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

7.5 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

SSVC

Exploitation: poc Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-06-11 14:01 UTC

CWE

CWE-400 - Uncontrolled Resource Consumption
CWE-770 - Allocation of Resources Without Limits or Throttling

Assigner

@huntr_ai mitre root

References

5 references

URL	Tags
https://huntr.com/bounties/7bd92629-b396-4449-8f8…
https://github.com/vllm-project/vllm/commit/58ee6…
https://access.redhat.com/security/cve/CVE-2026-5497	vdb-entryx_refsource_REDHAT
https://bugzilla.redhat.com/show_bug.cgi?id=2487813	issue-trackingx_refsource_REDHAT
https://security.access.redhat.com/data/csaf/v2/v…	x_sadp-csaf-vex

Impacted products

4 products

Vendor	Product	Version
vllm-project	vllm-project/vllm	Affected: unspecified , < 0.19.0 (custom)
Red Hat	Red Hat AI Inference Server	cpe:/a:redhat:ai_inference_server:3
Red Hat	Red Hat Enterprise Linux AI (RHEL AI) 3	cpe:/a:redhat:enterprise_linux_ai:3
Red Hat	Red Hat OpenShift AI (RHOAI)	cpe:/a:redhat:openshift_ai

Show details on NVD website

JSON

To clipboard

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      ],
      "source": {
        "advisory": "7bd92629-b396-4449-8f88-6c0092530eb4",
        "discovery": "EXTERNAL"
      },
      "title": "Unbounded Frame Count in video/jpeg Base64 Data URL Processing Leads to OOM DoS in vllm-project/vllm"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
    "assignerShortName": "@huntr_ai",
    "cveId": "CVE-2026-5497",
    "datePublished": "2026-06-11T08:31:18.953Z",
    "dateReserved": "2026-04-03T14:41:01.113Z",
    "dateUpdated": "2026-07-22T12:08:48.968Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2"
}

GHSA-WCWG-C5FC-9VRC

Vulnerability from github – Published: 2026-06-11 12:32 – Updated: 2026-08-18 20:16

Summary

vLLM is vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method

Details

Severity

7.5 (High)


                  
                    CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Show details on source website

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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "vllm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.8.0"
            },
            {
              "fixed": "0.19.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-5497"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-400",
      "CWE-770"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-08-18T20:16:47Z",
    "nvd_published_at": "2026-06-11T10:16:21Z",
    "severity": "HIGH"
  },
  "details": "vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.",
  "id": "GHSA-wcwg-c5fc-9vrc",
  "modified": "2026-08-18T20:16:47Z",
  "published": "2026-06-11T12:32:44Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-5497"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/commit/58ee61422169ce17e08248f8efa1e9df434fe395"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:33524"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:33531"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/security/cve/CVE-2026-5497"
    },
    {
      "type": "WEB",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2487813"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-2302.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/vllm-project/vllm"
    },
    {
      "type": "WEB",
      "url": "https://huntr.com/bounties/7bd92629-b396-4449-8f88-6c0092530eb4"
    },
    {
      "type": "WEB",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-5497.json"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "vLLM is vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

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Confirmed: The vulnerability has been validated from an analyst's perspective.
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Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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Action not permitted

PYSEC-2026-2302

CVE-2026-5497 (GCVE-0-2026-5497)

GHSA-WCWG-C5FC-9VRC

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Related by attack behaviour